Apparatus and method for monitoring parking area

ABSTRACT

Systems, apparatuses and methods are provided herein for monitoring a parking area. A system for monitoring a parking area comprises an image database storing a plurality of images of a parking lot taken by one or more satellites over time, a baseline database storing baseline models of a plurality of sections of the parking lot, and a control circuit coupled to the image database and the baseline database. The control circuit being configured to: determine a utilization condition for each of the plurality of sections of the parking lot based on performing image analysis on the plurality of images of the parking lot, compare the utilization condition for each of the plurality of sections of the parking lot with the baseline model of the plurality of sections of the parking lot, and in an event that the utilization condition of a section of the plurality of sections of the parking lot substantially deviates from the baseline model of the section, automatically generate an action recommendation for the section of the parking lot.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/340,860, filed Nov. 1, 2016, which claims the benefit of U.S.Provisional application No. 62/249,635, filed Nov. 2, 2015, both ofwhich are incorporated herein by reference in their entireties.

TECHNICAL FIELD

This invention relates generally to parking area management.

BACKGROUND

Retail stores and centers often include a parking area. A customer'sexperience with the parking area can affect the customer's overallshopping experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Disclosed herein are embodiments of apparatuses and methods formonitoring a parking area. This description includes drawings, wherein:

FIG. 1 is a block diagram of a system in accordance with severalembodiments.

FIG. 2 is a flow diagram of a method in accordance with severalembodiments.

FIG. 3 is an illustration of a parking lot image and section analysis inaccordance with several embodiments.

Elements in the figures are illustrated for simplicity and clarity andhave not necessarily been drawn to scale. For example, the dimensionsand/or relative positioning of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of various embodiments of the present invention. Also,common but well-understood elements that are useful or necessary in acommercially feasible embodiment are often not depicted in order tofacilitate a less obstructed view of these various embodiments of thepresent invention. Certain actions and/or steps may be described ordepicted in a particular order of occurrence while those skilled in theart will understand that such specificity with respect to sequence isnot actually required. The terms and expressions used herein have theordinary technical meaning as is accorded to such terms and expressionsby persons skilled in the technical field as set forth above exceptwhere different specific meanings have otherwise been set forth herein.

DETAILED DESCRIPTION

Generally speaking, pursuant to various embodiments, systems,apparatuses and methods are provided herein for monitoring a parkingarea. A system for monitoring a parking area comprises a satellite imagedatabase storing a plurality of satellite images of a parking lot takenby one or more satellites over time, a baseline database storingbaseline models of a plurality of sections of the parking lot, and acontrol circuit coupled to the satellite image database and the baselinedatabase. The control circuit being configured to: determine autilization condition for each of the plurality of sections of theparking lot based on performing image analysis on the plurality ofsatellite images of the parking lot, compare the utilization conditionfor each of the plurality of sections of the parking lot with thebaseline model of the plurality of sections of the parking lot, and inan event that the utilization condition of a section of the plurality ofsections of the parking lot substantially deviates from the baselinemodel of the section, automatically generate an action recommendationfor the section of the parking lot.

In some embodiments, parking lot satellite image analysis may be used bya store to evaluate the sufficiency of parking lot capacity, comparenearby stores, detect constantly filled spots, detect lot damage,determine traffic flow, determine delivery area efficiency, and/ordetect parking abuses from RVs, trucks, or non-customers. The system maytranslate multiple satellite images into data and compare the data withother statistical information such as sales, visits, costs, shipping,receiving, and labor associated with the stores.

Generally, the satellite images provides a “bird's eye view” of shoppingfacilities being managed. The system may direct a satellite to captureimages at selected locations. Images may be taken at a set frequencywith time limitations (e.g. day, night, peak hours) over a period oftime (days, weeks, months, years) and stored in a computer readablestorage medium. Image analysis may be performed on the satellite imagesto determine information such as 1) the number of cars in the lot, 2)the number of open parking spaces, 3) the location of each open andtaken parking space, 4) the number of spaces taken by cars versus othertypes of vehicles, 5) the number of spaces taken by non-vehicles (snow,products from lawn and garden, other items), 6) the number of spacestaken by cart corrals, 7) the number of spaces where shopping carts arepresent, 8) the number of trailers in the parking area, 9) which parkingspaces are premium spaces (spots that are most frequently filled), 10)the occupancy rate at each time slot, 11) the best time periods forexpedient shopping per demographic category specified (retirement area,soccer mom, collage town), and 12) the occupancy rate of the handicapspaces. The system may also store other known information about theparking area such as 1) the location of each space recorded, 2) thenumber of spaces the parking lot currently has, 3) the number ofentrances into the parking lot, and 4) the number of handicap spaces foranalysis.

In some embodiments, when the system determines that parking isconsistently over threshold capacity (e.g. 80%) during peak shoppinghours, the system may indicate that the facility has reach a limitationwhich needs correction. The system may automatically and periodicallyrecommend the expansion of the parking lot via a report to the storemanagers and/or supervisors. In some embodiments, the system may trackthe utilization rate of handicap spaces and compare it to governmentrequirements to ensure regulation compliance. The system may recommendincreasing or decreasing handicap spaces based on the comparison.

In some embodiments, the system may determine time periods with lowparking area utilization rates and automatically recommend promotions toencourage customers to shop during non-peak timeframes. The parking areautilization rate may be determined for different shopper demographics,and the promotions may be targeted toward select demographic groups. Insome embodiments, timeframes with low parking area utilization may becommunicated to customers via email, text, website, and/or a mobileapplication (“app”) as recommended times to visit the store. In someembodiments, a customer may sign up for a store mobile app and receivenotifications of recommended times to visit the store. In someembodiments, added shopping incentives such as discounts and coupons maybe made available during the recommended times.

In some embodiments, the system may identify drastic differences betweenstores and/or between time periods that need further investigation. Forexample, the system may compare the average number of cars in theparking area with the volume of sales for different stores. A store withhigh sales volume but few cars in the lot may indicate that the storehas an efficient checkout process while the converse may mean that thestore needs evaluation to determine the cause for the relatively highparking area utilization rate. A store with high sales and few cars inthe lot may be further evaluated to see if the parking area's lowutilization rate is managed by moving customers quickly in and out ofthe store with faster checkout lines. In some embodiments, the turnoverrate for each car may be calculated to determine visit durations forcustomers. A store with relatively low sales and high number of cars inthe lot may also be investigated for areas of improvement to reduce thevisit durations for customers. Practices may be identified from storeswith relative high sales and few cars in the lot and implemented in lessefficient stores to increase customer flow rate.

In some embodiments, the system may identify peak and low shopping timesbased on analyzing parking lot utilization rate. Peak times maycorresponds to a time period in a day (e.g. hours) and/or a day of theweek, etc. Abnormally high customer volumes may also be identified andinvestigated with other data for the area such as weather and/or localevents.

In some embodiments, the system may be configured to recommend one ormore of the following corrective actions: provide customer incentives toshop during non-peak times, increase worker numbers, and identifyunderutilized parking area sections. In some embodiments, a thresholdmay be set and a corrective action report may be sent to the storemanager and leadership with the following recommendations depending onthe image analysis: contact a towing service when the number of carsexceeding a permitted parking time limit reaches a threshold, clean upcarts when unreturned carts in the parking lot have exceeded athreshold, instruct associates to move their cars off prime spots whenspots designated for associate parking is not utilized but other parkingspots are constantly used beyond a threshold, and adjust cart corralplacing to free up premium spaces and/or to better cover the mostutilized parking areas.

In some embodiments, the system may detect parking lot damage based ondetecting customer's avoidance of those areas. The detected damaged areamay be automatically reported to store manager and leadership to remedy.Image analysis may further identify areas where directions not clearlymarked and/or heavy traffic areas around receiving, drive thru, and pickup areas. The system may be further be configured to perform capacitycomparison with nearby stores and traffic flow of the store area. Thesystem may use image analysis to identify gridlock or accidents bydetecting backup of traffic trying to exit the area and alert managementand leadership that the store may need to adjust traffic lights,entrances, etc.

In some embodiments, the system may monitor delivery, receiving, truckand trailer traffic and parking. The number of trailers and the lengthof time each trailer is parked in the same position may be tracked withsatellite images. The system may automatically generate a report andrecommend a corrective action if a threshold is exceeded. For example,the system may monitor the duration each trailer is parked on the lot,actively unloading, waiting to unload, and waiting to be picked up.

In some embodiments, the system may be configured to monitor and managegrocery pick up traffic. The parking area may designate special spotsfor customers utilizing a grocery pick up program. The system maycompare the actual utilization conditions of the grocery pick up areasto an expected use and capacity, and determine if additional spaces areneeded or if spaces should be repurposed for regular parking. The systemmay also determine that the grocery pick up program has a lowparticipation rate and may need more marketing. The effectiveness of thegrocery pickup program may be monitored based on occupancy rate of thedesignated spaces at peak times and/or the waiting time of cars parkedin the designated spaces.

Generally, the system provides a combination of automation in theretrieval and analysis of information. The system may utilize parkinglot satellite images to understand shopping facility usage and convertthe images to parking data to automatically recommend corrective actionsto store management and leadership. The system may further allow thestores to track the utilization conditions and recommendations over timeto understand the effectiveness of the recommendations and correctiveactions.

Referring now to FIG. 1, a system for monitoring a parking area isshown. The system 100 includes a control circuit 110 coupled to asatellite image database 120 storing images captured by a satellite 122and a baseline database 130. The control circuit 110 is configured todetermine and generate action recommendation 140.

The control circuit 110 may comprise a central processing unit, aprocessor, a microprocessor and the like and may be part of a server, acentral computing system, a cloud server, and the like. The controlcircuit 110 may be configured to execute computer readable instructionsstored on a computer readable storage memory (not shown). The computerreadable storage memory may comprise volatile and/or non-volatile memoryand has stored upon it a set of computer readable instructions which,when executed by the control circuit 110, causes the system to outputaction recommendation 140 based at least on the information in thesatellite image database 120 and the baseline database 130. Generally,the computer readable instructions may cause the control circuit 110 toperform one or more steps in the methods and processes described withreference to FIG. 2 herein.

The satellite image database 120 comprises computer readable memory andhas stored upon it multiple satellite images of one or more parkingareas captured by one or more satellites 122. The satellite 122 may beoperated or leased by a retail entity and be directed to periodicallycapture images of one or more parking areas. The images may be receivedvia a satellite receiver and may be forwarded to the retail entity froma satellite operator service. In some embodiments, the satellite 122 mayrepresent a system of multiple satellites orbiting the earth. Theparking area and parking lot may generally refer to the open air area ofa shopping facility and may include one or more of parking spots,roadways, pedestrian walkways, medians, landscaping areas, truck loadingand unloading areas, cart return areas, passenger loading areas, etc.

The baseline database 130 comprises computer readable memory and hasstored upon it one or more baseline models for one or more parkingareas. A baseline model for a parking area may comprise one or more of:overall utilization rate, utilization rate by timeframe (hour, day,week, month, etc.), overall vehicle turnover rate, vehicle turnover rateby timeframe, vehicle types, roadway flow rates, etc. These values maybe specified for different sections of a parking area. For example, autilization condition model may specify different values or value rangesfor all parking spots, a subset of parking spots, individual parkingspots, specialized parking spots (e.g. handicap spots, trailer spots, RVspots, etc.), pathways, roadways, truck loading zone, etc. In someembodiments, the baseline models may be based on historical values ofthe specific store or of a collection of stores, and/or may comprisevalues corresponding to an optimum condition. For example, the baselinemodel may be based on the store's average utilization condition and/orthe average utilization condition of similar store such that significantdeviation from the average utilization condition may be detected by thesystem. In some embodiments, the baseline model may be automaticallyupdated based on an average utilization condition from the specificstore and/or a collection of stores derived from on-going image analysisof satellite photos. In some embodiments, the baseline model may furtherspecify a deviation threshold that is used to determine whether thedeviation is sufficiently significant to generate an actionrecommendation. In some embodiments, different action recommendationsmay be generated based on the amount of deviation from the baselinemodel.

In some embodiments, the baseline database 130 and/or a separate storagedevice may store other analytics information accessible by the controlcircuit 110. For example, parking lot layout, parking space assignment,sales data for one or more stores, promotions at one or more stores,demographic information associated with each vehicle type, local trafficinformation, local weather information, holidays, etc. may also be usedby the control circuit 110 for determining and generating actionrecommendation 140.

Action recommendation 140 may comprise one or more of a user interfacedisplay, an email, a printed report, a spreadsheet, a pop-up alert, andthe like. In some embodiments, the action recommendation 140 may beincluded in a periodical report of a retail store. In some embodiments,the action recommendation 140 may be a pop-up notification and/ordisplayed information on a user interface program such as a storemanagement program, a point of sales system, and a customer mobileapplication. In some embodiments, the action recommendation may becommunicated via a network, such as the internet, to one or more of astore management program, a point of sales system, and a customer mobileapplication. In some embodiments, action recommendations 140 may furtherbe stored in a computer readable medium for further analysis. Forexample, the stored action recommendations 140 may be used to update thebaseline models, action recommendation triggering thresholds, and/or thealgorithm with which the control circuit 110 determines an actionrecommendation 140.

In some embodiments, one or more of the satellite image database 120,the baseline database 130, the memory device coupled to the controlcircuit 110, and the memory device for storing the action recommendation140 may be implemented on the same one or more memory devices orimplemented on two or more separate devices. The satellite imagedatabase 120, the baseline database 130, the memory device coupled tothe control circuit 110, and the memory device for storing the actionrecommendation 140 may comprise local, remote, networked, and/or cloudbased storage accessible by the control circuit 110.

Referring now to FIG. 2, a method of monitoring parking area is shown.In some embodiments, the steps shown in FIG. 2 may be performed by aprocessor-based device, such as the control circuit 110, executing a setof computer readable instructions.

In step 201, the system compiles multiple satellite images of a parkinglot taken over time by one or more satellites. The satellite images maybe provided by a satellite service provider and stored in a satelliteimage database 120. The satellite images may be captured by the samesatellite or by different satellites orbiting the earth. In someembodiments, the satellite images may comprise images captured bydifferent types of optical sensors, such as visible spectrum opticalsensors and infrared optical sensor. In some embodiments, the satelliteimages may generally be taken from the bird's-eye view of the parkinglot. The images may be compiled by isolating the images of the parkinglot from the captured images, matching corresponding sections of parkingareas to each other, and/or sorting images based on their capture time.In some embodiments, the images taken over time may be stitched into atime lapse video of the parking lot.

In step 202, the system determines a utilization condition for aplurality of sections of the parking lot. In some embodiments, theparking lot may be divided into a plurality of sections, such as:parking spots, a subset of parking spots, individual parking spots,specialized parking spots (e.g. handicap spots, trailer spots, RV spots,etc.), pathways, roadways, truck loading zone, etc. The utilizationcondition may comprises one or more of: overall utilization rate,utilization rate by timeframe (hour, day, week, month, etc.), overallvehicle turnover rate, vehicle turnover rate by timeframe, vehicle type,pathway flow rate, etc. An example of the section analysis is describedwith reference to FIG. 3 herein.

In some embodiments, the system may count the number of parked carsand/or empty parking spots in each satellite image of the parking lot todetermine the parking spot utilization rate (e.g. occupancy rate) duringdifferent times of a day, different days of a week, different days of amonth, different days of a year, different weeks of a year, etc. In someembodiments, the system may count the number of parked cars and/or emptyparking spots in separate sections of the parking lot and/or individualparking spots to determine the utilization rate of each of the sectionand/or spots over time. In some embodiments, the system may furtherdistinguish parking spots occupied by a parked car versus other types ofitems (e.g. cart carol, snow, store display, etc.) and determine theutilization rate based on the actual available parking spots.

In some embodiments, the system may determine characteristics ofindividual cars in the parking lot based on the satellite images. Insome embodiments, the system may keep track of cars parked in the samespot by their visual characteristics (e.g. color, shape, size, visiblemarkings, etc.). The system may then determine the turnover rate (i.e.how long each car is parked) for the parking lot, a subsection of theparking, and/or each individual parking spot. In some embodiments, thesystem may identify each vehicle as being one or more of a sedan, a SUV,a mini-van, a pickup truck, a RV, a truck and trailer, etc. In someembodiments, the system may further visually identify other features inthe parking lot such as cart return corral, charging stations, temporarytents and displays, etc. based on their respective visualcharacteristics. In some embodiments, the system may also visuallyidentify obstacles in the parking lot such as damaged pavement,abandoned carts, snow, large trash, etc.

In some embodiments, the system may determine flow information forpathways and roadways in and around the parking lot. For example, thesystem may measure how fast it takes for each car and/or pedestrian totravel through a segment of a pathway or roadway by tracking a carand/or a pedestrian over a plurality of satellite images. The flow ratemay be measured for roadways between parking spots, connecting parkingspots, which can be in front of the store, leading to the exit of thestore, etc. as well as public roadways around the store. The system mayidentify one or more of a damaged pavement condition, a narrow aislecondition, a congestion prone section, an accident prone section, and aheavy pedestrian traffic section in the parking lot based on the flowrate of various section of the parking lot.

In step 203, the system compares the utilization condition determined instep 202 with corresponding values in a baseline model for the monitoredparking lot. A baseline model may comprise one or more of: an overallutilization rate, a utilization rate by timeframe (hour, day, week,month, etc.), an overall vehicle turnover rate, a vehicle turnover rateby timeframe, a vehicle type, a pathway flow rate, etc. The baselinemodel may comprise expected utilization conditions for differentsections of a parking area and/or for different timeframes. For example,utilization conditions in the baseline model may specify differentvalues for parking spots, a subset of parking spots, individual parkingspots, specialized parking spots (e.g. handicap spots, trailer spots, RVspots, etc.), pathways, exit roadways, truck loading zones, etc. In someembodiments, the baseline models may be based on historical values ofthe specific store, of a collection of stores, and/or may be comprisedof values corresponding to an optimum and/or ideal condition. In someembodiments, the baseline model may be automatically updated based onaverage values derived from satellite images of the specific storeand/or a collection of stores. In some embodiments, the comparison instep 203 may further include other store related data such as storesales volume, store size, store shopper demographic, weather conditions,store staffing information, etc.

In step 204, the system generates an action recommendation based on thecomparison in step 203. In some embodiments, an action recommendationmay only be generated if the utilization condition determined in step202 deviates significantly (e.g. exceeds a threshold) from the baselinemodel. The action recommendation may be based on the type of utilizationcondition determined in step 202 that is out of tolerance from thebaseline model. In some embodiments, the system determines an actionrecommendation based on the amount of deviation detected. In someembodiments, the action recommendation may be automatically generated byselecting from a list of possible action recommendations based on thecomparison.

In some embodiments, the system may compare the utilization rate (e.g.percentage of filled parking spots) in the satellite images with theexpected utilization rate in the baseline model in step 203. If theutilization rate exceeds a threshold maximum utilization rate (e.g. 80%,90%, etc.) anytime and/or during a timeframe (e.g. peak shopping time)specified in the baseline model, the system may recommend increasing thenumber of available parking spaces. In some embodiments, the utilizationrate may further take into account obstructed spots due to abandonedcarts, snow, temporary installations, construction, road damage, largetrash, etc. For example, the utilization rate may be based on all spotsin the parking lot and/or be based on actual available parking spots asidentified in the satellite images. In some embodiments, the system mayfurther determine one or more methods for increasing parking spots basedon the analysis of parking lot images and/or other information fromother stores. For example, the system may recommend reducing and/orrelocation features such as cart corrals and temporary tents, reducingthe number of specialized parking slots, expanding into a surroundinglandscaped areas, etc. In some embodiments, the utilization rate ofspecialized spots may be separately tracked to determine the efficiencyof the specialized spots. The specialized spots may include one or moreof handicapped spots, internet order pick-up spots, expecting mothers'parking, clean air vehicles parking, charging station spots, etc. If theutilization rate of these specialized spots exceeds a threshold for aprolonged period and/or during peak hours, the system may recommendassigning additional spots as specialized spots. For handicap spots, thesystem may further compare the utilization condition with applicablegovernment requirements to ensure that the requirements are met. Forexample, the system may determine whether the handicap parking spots aresufficient based on one or more of the store's size, the parking lot'ssize, the number of total available spots, the store's customer volume,etc. In some embodiments, the action recommendation may be generatedbased on the duration and/or frequency of high utilization rateconditions. For example, an action recommendation may only be generatedif the utilization rate exceeds 80% of the lot capacity for 20% of thetime that the store is in operation.

If the utilization rate of a timeframe is consistently below theexpected and/or average utilization rate specified in the baselinemodel, the system may recommend promotions to store management and/orcustomers to encourage an increase of customer volume during slowperiods. In some embodiments, the system may automatically send outalerts and/or time restricted promotions to encourage customers to visitthe store during slow periods. For example, if the system detects thatthe utilization rate is particularly low between 8 am to 11 am onweekdays, the store may publish promotions (e.g. special markdowns,discounts) that only run between 8 am to 11 am on weekdays. In someembodiments, the low utilization periods and/or associated discounts maybe communicated to customers via website, emails, text message, mobileapplication, etc. With time restricted promotions, a customer may berewarded with discounts when they shop during a low utilization period.In some embodiments, the customer may download a mobile app and/oraccess a website to view the estimated parking lot occupancy rate todecide when they would like to visit a store. Generally, the utilizationrate may be used to identify peak and low shopping timeframes bycomparing the utilization rate in different time periods. In someembodiments, the average utilization rate for the store and/or specifictimeframes (hour, day, month, etc.) may be stored as the baseline model.When the system determines that a utilization rate that deviates fromthe baseline model for an amount exceeding a threshold (e.g. too high ortoo low), the system may generate an action recommendation for aninvestigation into the cause of the significant deviation. In someembodiments, the system may compare the utilization rate with localfactors such as road construction, weather, events, holidays, storepromotions, etc. to provide a suggested cause for the deviation. In someembodiments, the system may modify the baseline model and/or actionrecommendation triggering threshold based on known road constructions,weather events, special events, holidays, store promotions, etc.

In some embodiments, the system may further determine a ratio betweenutilization rate and store sales and/or customer volume for each storeand/or timeframe. For example, a ratio of sales volume per car parked inthe customer parking area of the parking lot may be determined for anumber of stores. The system may recommend an investigation of storeswith relatively low sales volume per parked car ratio to determinewhether more workers are needed to help with faster checkout and/or moreparking enforcement is needed to reduce unauthorized vehicles. Storeswith relatively high sales volume per parked car ratio may also bestudied to determine whether any practices of the store lead to theincreased efficiency. Any such practice may then be suggested forimplementation in other stores to increase efficiency. In someembodiments, the turnover rates of cars may be tracked with each car'svisual characteristics (e.g. size, shape, color, visible marking, etc.)as seen in the satellite images. The average turnover rate of the carsmay be compared to assess the relative efficiency of the stores (e.g.how fast the customers are able to complete their shopping trip). Thesales volume per car parked ratio and/or car turnover rate data mayfurther be combined with other store information such as staffinginformation (e.g. number of works assigned to each task) to determinewhether adjustments are needed for at least some time frames. In someembodiments, the system may recommend staffing adjustments and/ormanagement directive changes to less efficient stores.

In some embodiments, the system may track vehicle turnover rate forparking enforcement purposes. For example, the system may track aduration each car is parked in the same spot via satellite image anddetermine whether the duration exceeds the permitted duration for theparking lot. An action recommendation may be generated when a parkingviolation is detected (e.g. parked over 8 hours, etc.) The actionrecommendation may comprise contacting a towing service for removal ofthe vehicle. In some embodiments, the system may count the number ofcars with parking violation and only contact the towing service whenthat number exceeds a threshold (e.g. over 4 cars).

In some embodiments, the system may identify “premium” spots withsatellite image analysis. Premium spots may be spots that have arelatively high utilization rate as compared to other spots in the lot.The identified premium spots may be recommended for use for incentiveprograms such as special parking for interne order pick-ups, forexpecting mothers, for clean air vehicles, etc. In some embodiments, theaction recommendation generated by the system may include instructionsfor store associates to not park in the premium spots and/or use thepremium spots for outdoor features (e.g. temporary displays, tents).

In some embodiments, the system may determine one or more obstructionsin the parking lot from satellite image analysis. The obstructions maybe identified via visual characters of the obstruction and/or viadetecting unusual utilization or flow rate of the spots and/or the area.In some embodiments, the system may identify parking spots and/orroadways occupied by unreturned shopping cart(s), snow, large trash,etc. and recommend removal of the obstruction to store clerks. Thebaseline model may comprise an unobstructed appearance of a parkingspot, an expected normal utilization rate for one or more parking spots,and/or an expected normal flow rate for roadways. In some embodiments,the system may identify a section of the parking lot with a high numberand/or high frequency of unreturned carts, and recommend the additionand/or relocation of cart corrals. In some embodiments, the system maypriority obstruction clearing recommendation for identified premiumspots.

In some embodiments, the system may determine the flow rate of roadwaysand pathways of the parking lot by counting how many vehicles and/orpedestrians pass through a section during a set time period (e.g. carsper minute). The flow rate may be used to identify one or more of: adamaged pavement condition, a narrow aisle condition, a congestion pronesection, an accident prone section, and a heavy pedestrian trafficsection in the parking lot. In some embodiments, the system may alsodetermine congestion conditions by the flow rate and/or by trackingindividual cars' travel through roadways. The system may further recordany collisions involving vehicles and/or pedestrians. The baselinecondition model may include an expected flow rate for the roadways. Adetected flow rate that is significantly below the expected flow ratemay trigger an action recommendation. For sections with frequentcongestion and/or collision conditions, the system may recommendrerouting car or foot traffic in the area, reassigning at least someparking spots, adjusting the signage, etc. to reduce congestion andcollision. In some embodiments, if the system detects frequentcongestion near exits of the parking lot, the system may recommendadjusting the number and/or location of exit points, and/or contactinglocal government to request adjustments to the public roadway. In someembodiments, the system may determine that the cars tend avoid an areaof the parking lot and/or roadway through the flow rate information. Thesystem may then recommend an investigation of pavement damage for theavoided section.

In some embodiments, the system may track the flow rate of truck andtrailer traffic in the parking area. In some embodiments, the system maydetermine the unloading speed of each truck by measuring how long atruck is parked at the unloading bay and/or how long the trucked isparked in a waiting area waiting to unload. If the duration that a truckand trailer remains in a waiting spot and/or unloading bay exceeds athreshold specified in the baseline model, the system may recommend anadjustment of unloading dock worker assignments.

In some embodiments, the system may determine one or more vehicle types.For example, the system may determine whether a vehicle is a sedan, RV,SUV, pickup truck, truck and trailer, etc. based on image analysis. Insome embodiments, utilization conditions for each type of vehicle may bedetermined separately. In some embodiments, the system may recommendadjusting the number of parking spaces allotted for each vehicle typebased on the utilization conditions for each vehicle type. In someembodiments, the vehicles types may be used by the system to derivedemographic information for each store and generate targeted promotionsand/or advertisements for that store. The vehicle type compositions mayalso be determined for different time periods to derive the shoppinghabits of different demographic groups for targeted promotions. In someembodiments, the identified vehicle types may be used for parking rulesenforcement. For example, the system may identify whether large vehiclessuch as RVs and truck and trailers are parked in designated spots. Ifviolations are detected, the system may recommend contacting a towingservice for removal.

In some embodiments, after step 204, the action recommendations may bestored for further analysis. For example, utilization conditions beforeand after a recommended action is carried out may be compared to assessthe effectiveness of the recommended action. Baseline models and/oralgorithms for determining the action recommendation may be modifiedbased on the determined effectiveness. For example, the reportingthresholds in the baseline models may be adjusted so that conditionscould benefit from corrective actions may not be under-reported orover-reported. In some embodiments, different types of correctiveactions may be compared to each other for effectiveness, and the moreeffective actions may be prioritized in future recommendations.

Referring now to FIG. 3, an illustration of a parking lot image andsection analysis in accordance with several embodiments is shown.Satellite image 310 represents an image of a monitored parking lot. Theimage 310 may be cropped from a larger image and/or may be stitchedtogether form two or more satellite images. The section analysis map 320may be stored in the baseline model database and/or be derived fromimage analysis of the satellite image 310. One or more baselineutilization condition types and values may be associated with each ofthe sections shown in the section analysis map 320. For example, abaseline model may include a baseline occupancy rate and/or turnoverrate for all of the parking areas, a subset of the parking areas, eachof the parking areas, and/or each individual parking spots in theparking areas shown in the section analysis map 320. The baseline modelmay also include expected vehicles types in a subset of the parkingareas, each of the parking areas, and/or each individual parking spots.The baseline model may also include a baseline vehicle and/or pedestrianflow rate for the entire parking lot, a subset of the roadways, and/orseparate roadway sections. In some embodiments, the baseline model mayfurther specify the expected turnover rate for truck and trailers in thetruck unloading area. The baseline utilization condition values may alsobe specified for different timeframes (hours of the day, day of theweek, day of the month, month of the year, etc.)

Generally, the section analysis map 320 may specify the utilizationconditions (e.g. utilization rate, turnover rate, vehicle type, etc.) tobe determined from the satellite image 310. The system then sections thesatellite images based on the baseline model and determines thespecified utilization conditions by performing image analysis ofmultiple satellite images of the parking area. The determinedutilization conditions are compared with the values of the correspondingsection in the baseline model. If the actual utilization conditiondeviates substantially (e.g. exceeding a value or percentage threshold)from the baseline model for any of the sections, the system maydetermine an action recommendation for that section and/or for theentire parking lot.

The satellite image 310 and the section analysis map 320 are provided asexamples only. A monitored parking lot may be of any shape and includeany number of parking spots. The system may also monitor multiplephysically separated parking areas. The section analysis map 320 mayinclude more or fewer sections. One or more section may also be combinedor subdivided in the baseline model and for image analysis. In someembodiments, each individual parking spot may comprise a monitoredsection. The section analysis map 320 may further distinguishspecialized parking spots such as cart corrals, handicap spots, RVparking spots, clean air vehicles spots, online order pick-up spots,etc. for separate analysis.

With the methods, systems, and apparatuses described herein, satelliteimages may be used to better understand and manage parking lot usage.The system may be used to monitor parking areas, combine parking lotdata with other store information, and provide action recommendation forimprovements of parking lot design and store operation.

In one embodiment, a system for monitoring a parking area comprises asatellite image database storing a plurality of satellite images of aparking lot taken by one or more satellites over time, a baselinedatabase storing baseline models of a plurality of sections of theparking lot, and a control circuit coupled to the satellite imagedatabase and the baseline database. The control circuit being configuredto: determine a utilization condition for each of the plurality ofsections of the parking lot based on performing image analysis on theplurality of satellite images of the parking lot, compare theutilization condition for each of the plurality of sections of theparking lot with the baseline model of the plurality of sections of theparking lot, and in an event that the utilization condition of a sectionof the plurality of sections of the parking lot substantially deviatesfrom the baseline model of the section, automatically generate an actionrecommendation for the section of the parking lot.

In one embodiment, a method for monitoring a parking area comprises:compiling a plurality of satellite images of a parking lot stored in asatellite image database, the plurality of satellite images of theparking lot being taken by one or more satellites over time, determininga utilization condition for a plurality of sections of the parking lotbased on performing image analysis on the plurality of satellite imagesof the parking lot, comparing the utilization condition for each of theplurality of sections of the parking lot with a baseline model of eachof the plurality of sections of the parking lot stored in a baselinedatabase, and in an event that the utilization condition of a section ofthe plurality of sections of the parking lot substantially deviates fromthe baseline model of the section, automatically generating an actionrecommendation for the section of the parking lot.

In one embodiment, an apparatus for monitoring a parking area comprises:a non-transitory storage medium storing a set of computer readableinstructions, a control circuit configured to execute the set ofcomputer readable instructions which causes the control circuit to:compile a plurality of satellite images of a parking lot stored in asatellite image database, the plurality of satellite images of theparking lot being taken by one or more satellites over time, determine autilization condition for each of a plurality of sections of the parkinglot based on performing image analysis on the plurality of satelliteimages of the parking lot, compare the utilization condition for theplurality of sections of the parking lot with a baseline model of eachof the plurality of sections of the parking lot stored in a baselinedatabase, and in an event that the utilization condition of a section ofthe plurality of sections of the parking lot substantially deviates fromthe baseline model of the section, automatically generate an actionrecommendation for the section of the parking lot.

Those skilled in the art will recognize that a wide variety of othermodifications, alterations, and combinations can also be made withrespect to the above described embodiments without departing from thescope of the invention, and that such modifications, alterations, andcombinations are to be viewed as being within the ambit of the inventiveconcept.

What is claimed is:
 1. A system for monitoring a parking areacomprising: an image database storing a plurality of images of a parkinglot taken by one or more satellites over time; a baseline databasestoring baseline models of a plurality of sections of the parking lot;and a control circuit coupled to the image database and the baselinedatabase, the control circuit being configured to: determine autilization condition for each of the plurality of sections of theparking lot based on performing image analysis on the plurality ofimages of the parking lot; compare the utilization condition for each ofthe plurality of sections of the parking lot with a baseline model ofthe plurality of sections of the parking lot; and in an event that theutilization condition of a section of the plurality of sections of theparking lot exceeds a deviation threshold of deviates from the baselinemodel of the section, automatically generate an action recommendationfor the section of the parking lot; wherein the section corresponds to aloading zone of a shopping facility and the utilization conditioncorresponding to a turnover rate for one or more trucks.
 2. The systemof claim 1, wherein the action recommendation comprises an adjustment ofunloading dock worker assignments based on the turnover rate for the oneor more trucks.
 3. The system of claim 1, wherein the control circuit isfurther configured to identify a vehicle type for one or more vehiclesin the plurality of images of the parking lot, and wherein utilizationconditions are determined for each vehicle type.
 4. The system of claim3, wherein the action recommendation comprises adjusting a number ofparking spaces allotted for each vehicle type based on the utilizationconditions for each vehicle type.
 5. The system of claim 1, wherein thecontrol circuit is further configured to identify one or more of adamaged pavement condition, a narrow aisle condition, a congestion pronesection, an accident prone section, and a heavy pedestrian trafficsection in the parking lot based on the utilization condition.
 6. Amethod for monitoring a parking area comprising: compiling a pluralityof images of a parking lot stored in an image database, the plurality ofimages of the parking lot being taken by one or more satellites overtime; determining a utilization condition for a plurality of sections ofthe parking lot based on performing image analysis on the plurality ofimages of the parking lot; comparing the utilization condition for eachof the plurality of sections of the parking lot with a baseline model ofeach of the plurality of sections of the parking lot stored in abaseline database; and in an event that the utilization condition of asection of the plurality of sections of the parking lot exceeds adeviation threshold of deviates from the baseline model of the section,automatically generating an action recommendation for the section of theparking lot; wherein the section corresponds to a loading zone of ashopping facility and the utilization condition corresponding to aturnover rate for one or more trucks.
 7. The method of claim 6, whereinthe action recommendation comprises an adjustment of unloading dockworker assignments based on the turnover rate for the one or moretrucks.
 8. The method of claim 6, further comprising: identifying avehicle type for one or more vehicles in the plurality of images of theparking lot, and wherein utilization conditions are determined for eachvehicle type.
 9. The method of claim 8, wherein the actionrecommendation comprises adjusting a number of parking spaces allottedfor each vehicle type based on the utilization conditions for eachvehicle type.
 10. The method of claim 6, further comprising: identifyingone or more of a damaged pavement condition, a narrow aisle condition, acongestion prone section, an accident prone section, and a heavypedestrian traffic section in the parking lot based on the utilizationcondition.
 11. An apparatus for monitoring a parking area comprising: anon-transitory storage medium storing a set of computer readableinstructions; a control circuit configured to execute the set ofcomputer readable instructions which causes to the control circuit to:compile a plurality of images of a parking lot stored in an imagedatabase, the plurality of images of the parking lot being taken by oneor more satellites over time; determine a utilization condition for eachof a plurality of sections of the parking lot based on performing imageanalysis on the plurality of images of the parking lot; compare theutilization condition for the plurality of sections of the parking lotwith a baseline model of each of the plurality of sections of theparking lot stored in a baseline database; and in an event that theutilization condition of a section of the plurality of sections of theparking lot exceeds a deviation threshold of deviates from the baselinemodel of the section, automatically generate an action recommendationfor the section of the parking lot; wherein the section corresponds to aloading zone of a shopping facility and the utilization conditioncorresponding to a turnover rate for one or more trucks.
 12. A systemfor monitoring a parking area comprising: an image database storing aplurality of images of a parking lot taken by one or more satellitesover time; a baseline database storing baseline models of a plurality ofsections of the parking lot; and a control circuit coupled to the imagedatabase and the baseline database, the control circuit being configuredto: determine a utilization condition for each of the plurality ofsections of the parking lot based on performing image analysis on theplurality of images of the parking lot; compare the utilizationcondition for each of the plurality of sections of the parking lot witha baseline model of the plurality of sections of the parking lot; and inan event that the utilization condition of a section of the plurality ofsections of the parking lot exceeds a deviation threshold of deviatesfrom the baseline model of the section, automatically generate an actionrecommendation for the section of the parking lot; wherein the controlcircuit is further configured to identify a vehicle type for one or morevehicles in the plurality of images of the parking lot, and whereinutilization conditions are determined for each vehicle type; and whereinthe action recommendation comprises adjusting a number of parking spacesallotted for each vehicle type based on the utilization conditions foreach vehicle type.
 13. A method for monitoring a parking areacomprising: compiling a plurality of images of a parking lot stored inan image database, the plurality of images of the parking lot beingtaken by one or more satellites over time; determining a utilizationcondition for a plurality of sections of the parking lot based onperforming image analysis on the plurality of images of the parking lot;comparing the utilization condition for each of the plurality ofsections of the parking lot with a baseline model of each of theplurality of sections of the parking lot stored in a baseline database;in an event that the utilization condition of a section of the pluralityof sections of the parking lot exceeds a deviation threshold of deviatesfrom the baseline model of the section, automatically generating anaction recommendation for the section of the parking lot; andidentifying a vehicle type for one or more vehicles in the plurality ofimages of the parking lot, and wherein utilization conditions aredetermined for each vehicle type; wherein the action recommendationcomprises adjusting a number of parking spaces allotted for each vehicletype based on the utilization conditions for each vehicle type.